Tensorflow is a deep learning library developed by Google with a user friendly API that allows users to build machine learning models easily. Tensorflow is available on Knot only for the CPU mode unless you run interactively on the node knot-gpu2.cnsi.ucsb.edu ( you can ssh directly to knot-gpu2 ). Knot-gpu2 has a Titan V, a GTX 1080 Ti, and a P100. There are no longer any other GPUs on knot.
We recommend using conda from anaconda to run Tensorflow on knot-gpu2. So first, install anaconda (if you haven't already) from https://www.anaconda.com/download/#linux . Then issue a
conda create --name tf_gpu tensorflow-gpu
That will create an environment named tf_gpu for use with your python scripts. Note that it will take a while for conda to work its magic. After it finishes you can call your Tensorflow environment with a
source activate tf_gpu
That's it!
Tensorflow on CPU runs in a container based on Singularity, and uses the Ubuntu kernel.
Instructions
To use tensorflow include the following lines in your .bashrc (or .profile)
export PATH=/sw/csc/singularity/bin/:$PATH export LD_LIBRARY_PATH=/sw/csc/singularity/lib/singularity:$LD_LIBRARY_PATH
This is pretty much what you need to do!
Example
Below is a simple example code adapted from A. Damien's repository. This example builds a simple linear regression model using the computational graph scheme in Tensorflow
from __future__ import print_function import tensorflow as tf import numpy rng = numpy.random # Parameters learning_rate = 0.01 training_epochs = 1000 display_step = 50 # Training Data train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167, 7.042,10.791,5.313,7.997,5.654,9.27,3.1]) train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221, 2.827,3.465,1.65,2.904,2.42,2.94,1.3]) n_samples = train_X.shape[0] # tf Graph Input X = tf.placeholder("float") Y = tf.placeholder("float") # Set model weights W = tf.Variable(rng.randn(), name="weight") b = tf.Variable(rng.randn(), name="bias") # Construct a linear model pred = tf.add(tf.multiply(X, W), b) # Mean squared error cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples) # Gradient descent # Note, minimize() knows to modify W and b because Variable objects are trainable=True by default optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Initializing the variables init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: sess.run(init) # Fit all training data for epoch in range(training_epochs): for (x, y) in zip(train_X, train_Y): sess.run(optimizer, feed_dict={X: x, Y: y}) # Display logs per epoch step if (epoch+1) % display_step == 0: c = sess.run(cost, feed_dict={X: train_X, Y:train_Y}) print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \ "W=", sess.run(W), "b=", sess.run(b)) print("Optimization Finished!") training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y}) print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n') # Testing example, as requested (Issue #2) test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1]) test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03]) print("Testing... (Mean square loss Comparison)") testing_cost = sess.run( tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]), feed_dict={X: test_X, Y: test_Y}) # same function as cost above print("Testing cost=", testing_cost) print("Absolute mean square loss difference:", abs( training_cost - testing_cost))
Suppose the name of this file is
linear.py
Then, what you need is to include, in the same folder, is to have a job submission script (suppose it is called submit.job):
#!/bin/bash #PBS -l nodes=1:ppn=12 #PBS -l walltime=1:00:00 #PBS -N TFlinear #PBS -V # Make sure that you are in the job submission directory cd $PBS_O_WORKDIR singularity exec /sw/csc/SingularityImg/ubuntu_w_TFlowKeras.img python linear.py > out.log
There are several points which require some attention:
- Notice that we do not call the python on the host, but rather use the singularity container we built (ubuntu_w_TFlow.img). The job will fail without it.
- We cannot use more than 1 node. This image does not contain the MPI utilized version of Tensorflow (which has just recently been released and we have not tested it yet).
- Notice that the container image uses Python 2.7.
Then, simply submit your job to the queue by
qsub submit.job